Call for Papers:Vol.10 Issue.6

Submission
Last date		 30-Dec-2024
Acceptance Status In One Day
Paper Publish In Two Days
Submit ManuScript

News & Updates

Submit Article

Dear Authors, Article publish in our journal for Volume-10,Issue-6. For article submission on below link: Submit Manuscript


Join As Board

Dear Reviewer, You can join our Reviewer team without given any charges in our journal. Submit Details on below link: Join As Board


Paper Publication Charges

IJARIIE APP
Download Android App

For Authors

Archives

IJARIIE Board

Downloads

Android App

Download IJARIIE APP

Authors

Title: :  Handwritten Signature Verification System using machine Learning Approach
PaperId: :  14672
Published in:   International Journal Of Advance Research And Innovative Ideas In Education
Publisher:   IJARIIE
e-ISSN:   2395-4396
Volume/Issue:    Volume 7 Issue 3 2021
DUI:    16.0415/IJARIIE-14672
Licence: :   IJARIIE is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author NameAuthor Institute
Pooja GaikwadMET’s Institute of Engineering, Nashik
Kashaf PathanMET’s Institute of Engineering, Nashik
Purva PatilMET’s Institute of Engineering, Nashik
Prof. R.P. Dahake5MET’s Institute of Engineering, Nashik
Laxmi PagareMET’s Institute of Engineering, Nashik

Abstract

Information Technology
Offline handwritten signature, classification, algorithms, artificial intelligence, CNN
In the field of biometric, offline handwritten signature verification is most referenced procedure for authentication of a person during financial transaction. A signature is the “seal of approval” for verifying the approval of a person and remains the most preferred means of verification. This verification system mainly aims at verifying the discriminating the forged signature from the genuine signatures. In this work, Convolutional Neural Networks (CNN) have been used to learn features from the pre-processed genuine signatures and forged signatures dataset. The CNN used is inspired by Inception V1 architecture (GoogleNet). The architecture uses the concept of having different filters on same level so that the network would be wider instead of deeper. In this paper, the proposed model is tested on few publicly available datasets on kaggle.com. has been successful in verifying handwritten signature images provided with an extensive precision level.

Citations

Copy and paste a formatted citation or use one of the links to import into a bibliography manager and reference.

IJARIIE Pooja Gaikwad, Kashaf Pathan, Purva Patil, Prof. R.P. Dahake5, and Laxmi Pagare. "Handwritten Signature Verification System using machine Learning Approach" International Journal Of Advance Research And Innovative Ideas In Education Volume 7 Issue 3 2021 Page 3284-3289
MLA Pooja Gaikwad, Kashaf Pathan, Purva Patil, Prof. R.P. Dahake5, and Laxmi Pagare. "Handwritten Signature Verification System using machine Learning Approach." International Journal Of Advance Research And Innovative Ideas In Education 7.3(2021) : 3284-3289.
APA Pooja Gaikwad, Kashaf Pathan, Purva Patil, Prof. R.P. Dahake5, & Laxmi Pagare. (2021). Handwritten Signature Verification System using machine Learning Approach. International Journal Of Advance Research And Innovative Ideas In Education, 7(3), 3284-3289.
Chicago Pooja Gaikwad, Kashaf Pathan, Purva Patil, Prof. R.P. Dahake5, and Laxmi Pagare. "Handwritten Signature Verification System using machine Learning Approach." International Journal Of Advance Research And Innovative Ideas In Education 7, no. 3 (2021) : 3284-3289.
Oxford Pooja Gaikwad, Kashaf Pathan, Purva Patil, Prof. R.P. Dahake5, and Laxmi Pagare. 'Handwritten Signature Verification System using machine Learning Approach', International Journal Of Advance Research And Innovative Ideas In Education, vol. 7, no. 3, 2021, p. 3284-3289. Available from IJARIIE, https://ijariie.com/AdminUploadPdf/Handwritten_Signature_Verification_System_using_machine_Learning_Approach_ijariie14672.pdf (Accessed : ).
Harvard Pooja Gaikwad, Kashaf Pathan, Purva Patil, Prof. R.P. Dahake5, and Laxmi Pagare. (2021) 'Handwritten Signature Verification System using machine Learning Approach', International Journal Of Advance Research And Innovative Ideas In Education, 7(3), pp. 3284-3289IJARIIE [Online]. Available at: https://ijariie.com/AdminUploadPdf/Handwritten_Signature_Verification_System_using_machine_Learning_Approach_ijariie14672.pdf (Accessed : )
IEEE Pooja Gaikwad, Kashaf Pathan, Purva Patil, Prof. R.P. Dahake5, and Laxmi Pagare, "Handwritten Signature Verification System using machine Learning Approach," International Journal Of Advance Research And Innovative Ideas In Education, vol. 7, no. 3, pp. 3284-3289, May-Jun 2021. [Online]. Available: https://ijariie.com/AdminUploadPdf/Handwritten_Signature_Verification_System_using_machine_Learning_Approach_ijariie14672.pdf [Accessed : ].
Turabian Pooja Gaikwad, Kashaf Pathan, Purva Patil, Prof. R.P. Dahake5, and Laxmi Pagare. "Handwritten Signature Verification System using machine Learning Approach." International Journal Of Advance Research And Innovative Ideas In Education [Online]. volume 7 number 3 ().
Vancouver Pooja Gaikwad, Kashaf Pathan, Purva Patil, Prof. R.P. Dahake5, and Laxmi Pagare. Handwritten Signature Verification System using machine Learning Approach. International Journal Of Advance Research And Innovative Ideas In Education [Internet]. 2021 [Cited : ]; 7(3) : 3284-3289. Available from: https://ijariie.com/AdminUploadPdf/Handwritten_Signature_Verification_System_using_machine_Learning_Approach_ijariie14672.pdf
BibTex EndNote RefMan RefWorks

Number Of Downloads



Save in Google Drive

Similar-Paper

TitleArea of ResearchAuther NameAction
A Review on Ad Makers- Creation & Design Advertising CampaignsComputer EngineeringSwapnil Ohol1 Download
DETECTING FACIAL FORGERIES USING NOVEL FREQUENCY CONVOLUTIONAL NEURAL NETWORKComputer ScienceChikati Madhava Rao Download
Detecting Diabetes Early and Classifying Risk with Interpretable Deep Learning ModelsComputer ScienceMohmad Ahmed Ali Download
Enhance Accuracy of Medical Disease Prediction Using Machine Learning TechniquesMachine LearningSandeep Singh Sikarwar Download
Transformer-based Generative Adversarial Network (GAN) hybrid model for sentiment analysisInformation Technologyhridesh kumar Download
A Step Ahead in Operational Resilience - Integrating Observability with DevOpsComputer EngineeringAmit Sengupta Download
The Influence of Artificial Intelligence and Human-Computer Interaction on Enhancing Children's Learning ExperiencesComputer Engineering,Supriya S Kendhuli Download
Medicinal Plants Identification Using Deep LearningComputer Engineering Rohan Kumar Verma Download
“How to balance automation and ethics when rolling out AI automation in an Educational Framework”Computer SciecneSudhakar mishra Download
Microservices Fault Tolerance PatternsComputer EngineeringAmit Sengupta Download
Forecasting and Categorization of Cardiac Arrhythmia using Soft Computational ApproachesComputer Science and EngineeringDr. Sivaprakash. C Download
Enhancing CybersecurityComputer EngineeringG Tarun Download
Microservices Design PatternsComputer EngineeringAmit Sengupta Download
The Novel Approach to Detect Kidney Stones Using Deep Learning and Convolutional Neural NetworksCSEN Manjunatha Download
An investigation into the use of deep learning and image processing in the domain of diabetic medical careCSEHR Ravikumar Download
12